Making credit decisions under uncertainty may be approached in principle by a process known as estimation, i.e., estimating potential future outcomes resulting from decision alternatives. Potential outcomes may vary for different individuals based on many variables, including credit score, risk score, behavior score, age, property ownership, and other variables associated with the individuals. However, when estimation is performed, individuals may be categorized into different groups that may be associated with different covariates. Accordingly, a selection bias may be performed to deal with an issue of multiple covariates. However, the selection bias carries inaccuracies and other problems. Therefore, to deal with the issues of selection bias, there is a need for a method that uses a single variable instead of multiple covariates, where the single variable may encompass the effect of the multiple covariates.
Furthermore, conventional estimation techniques are often based on intuition and estimates made by these conventional techniques may not conform to empirical results that can be obtained by adequate historic testing. For example, it may be intuitive that individuals lying within a high income group may have a higher probability of taking a loan at a particular interest rate premium as compared to an individual within a lower income group. However, if sufficient historic testing is performed, the aforementioned intuition may not hold true, being that each individual may be associated with multiple covariates rather than just one covariate. Thus, a complex multidimensional problem due to multiple covariates needs to be solved. Accordingly, there is a need to perform estimation of potential outcomes in a convenient way such that estimation in consideration of all the covariates can be fairly represented by a corresponding estimation in consideration of a single variable that takes into account the effect of all the covariates.